Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/119725
|
| Title: | 深度學習應用於股價走勢之研究：以大陸市場為例
An Empirical Study of Deep Learning to the Trend of Stock Price in China Market |
| Authors: | 張力元
Chang, Li-Yuan |
| Contributors: | 黃泓智
Huang, Hong-Chih
張力元
Chang, Li-Yuan |
| Keywords: | 大陸股市
深度學習
股價走勢
技術指標
China stock market
Deep learning
Stock price trend
Technical indicators |
| Date: | 2018 |
| Issue Date: | 2018-08-29 15:49:15 (UTC+8) |
| Abstract: | 股價的未來走勢一直是一個未知且令人充滿興趣的研究領域，過去已有許多學者提出各種理論以論述其觀點，如今我們身處於人工智慧的時代，各種機器學習的應用已顛覆我們對生活方式的認知。本文建構一套神經網路的簡單序列模型，以幾種常見的技術指標為主要特徵，並選定未來二十日的股價漲跌作為預測目標，同時考慮交易成本，使用定錨式移動視窗的方式，將兩者之間的關係透過神經網路進行深度學習，藉以預測未來一年股價走勢的分類情況，從而挑選出具有上漲潛力的股票，以其分類結果作為判斷買賣時機的依據，將模型預測上漲機率較高的前幾檔股票納入投資組合，以實現自動化的資產配置，同時也考慮不同情境下的配置方式。實證結果顯示本文的主要策略相比大盤績效，其年化報酬率在大多數的年度皆有不錯表現，在七年回測期間的年化報酬率達13.67%，惟其標準差也稍高。
The future trend of stock prices has always been an unknown and interesting research field. Many scholars have proposed various theories to discuss their views. Now we are in the era of artificial intelligence, and the various application of machine learning has subverted our perception of lifestyle. This paper constructs a simple sequential model of neural network, with several common technical indicators as the main features, and selects the rise or fall of the stock prices in the next twenty days as the predicting target, while considering the transaction cost and using the anchored moving window method. The relationship between this two is deep learning through the neural network to predict the classification of stock price movements in the coming year, so as to select stocks with rising potential, and use the classification results as a basis for judging the timing of trading. The model predicting the first few stocks with higher probability are included in the portfolio to achieve automated asset allocation, while considering the configuration in different scenarios. The empirical results show that the main strategy of this paper has a good performance in most years compared to the market performance. The annualized rate of return during the seven-year back-testing period is 13.67%, but the standard deviation is also slightly higher. |
| Reference: | Akita, R., Yoshihara, A., Matsubara, T., & Uehara, K. (2016). Deep learning for stock prediction using numerical and textual information. In Computer and Information Science (ICIS), 2016 IEEE/ACIS 15th International Conference on (pp. 1-6). IEEE.
Chong, T. & Ng, W. (2008). Technical analysis and the London stock exchange: testing the MACD and RSI rules using the FT30. Applied Economics Letters, 15(14), 1111-1114.
Dean, J. (2016). Building machine learning systems that understand. In Proceedings of the 2016 International Conference on Management of Data (pp. 1-1). ACM.
Huval, B., Wang, T., Tandon, S., Kiske, J., Song, W., Pazhayampallil, J., & Mujica, F. (2015). An Empirical Evaluation of Deep Learning on Highway Driving. arXiv preprint arXiv:1504.01716.
Ioffe, S. & Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv preprint arXiv:1502.03167.
Khan, A. & Gour, B. Neural Networks with Technical Indicators Identify Best Timing to Invest in the Selected Stocks.
Kingma, D. & Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980.
Mizuno, H., Kosaka, M., Yajima, H., & Komoda, N. (1998). Application of Neural Network to Technical Analysis of Stock Market Prediction. Studies in Informatic and control, 7(3), 111-120.
Nelson, D., Pereira, A., & Oliveira, R. (2017). Stock Market`s Price Movement Prediction with LSTM Neural Networks. In Neural Networks (IJCNN), 2017 International Joint Conference on (pp. 1419-1426). IEEE.
Poulos, J. (2015). Predicting Stock Market Movement with Deep RNNs.
Sezer, O., Ozbayoglu, A., & Dogdu, E. (2017). An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework. In Proceedings of the SouthEast Conference (pp. 223-226). ACM.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. The Journal of Machine Learning Research, 15(1), 1929-1958.
Sun, Y., Wang, X., & Tang, X. (2014). Deep Learning Face Representation from Predicting 10,000 Classes. In Proceedings of the IEEE conference on computer vision and pattern recognition(pp. 1891-1898).
Wang, D., Khosla, A., Gargeya, R., Irshad, H., & Beck, A. (2016). Deep Learning for Identifying Metastatic Breast Cancer. arXiv preprint arXiv:1606.05718.
Xiong, R., Nichols, E., & Shen, Y. (2015). Deep Learning Stock Volatility with Google Domestic Trends. arXiv preprint arXiv:1512.04916. |
| Description: | 碩士
國立政治大學
風險管理與保險學系
105358012 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0105358012 |
| Data Type: | thesis |
| DOI: | 10.6814/THE.NCCU.RMI.009.2018.F08 |
| Appears in Collections: | [風險管理與保險學系] 學位論文
|
Files in This Item:
| File |
Size | Format | |
|---|
| 801201.pdf | 2615Kb | Adobe PDF2 | 2414 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|
